Infrared Image Fault Recognition Method for Disconnector Based on HOG Features
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摘要:
为了实现对于隔离开关红外图像的故障识别，本文利用改进SLIC算法，在颜色空间转换的基础上，对隔离开关的故障区域进行分割和标记，并有效提高图像分割精度。在HOG特征提取的基础上利用支持向量机算法，对隔离开关的红外图像进行设备和分类，区分其是否工作在正常状态，对于正常状态下的隔离开关，利用相对温差法，实现其故障状态的判断，相对温差越大，则故障越严重。通过实验证明，在优化的HOG特征参数情况下，可以实现图像设备的准确率最高，利用红外图像的故障诊断，可以对隔离开关的故障缺陷程度加以判断，并提供检修建议，本文模型具有很好的准确性和可靠性。
Abstract:To realize the fault recognition of an infrared image of a disconnector, this study uses an improved SLIC algorithm to segment and mark the fault area of the disconnector based on color space conversion. As a result, image segmentation accuracy was significantly improved. Based on HOG feature extraction, the support vector machine algorithm is used to classify an infrared image of a disconnector and determine whether it works in the normal state. For the disconnector in the normal state, the relative temperature difference method is used to determine its fault state. The greater the relative temperature difference, the more serious the fault. The experiments demonstrate that optimal HOG characteristic parameters yield the maximum accuracy of the imaging equipment. The fault diagnosis of an infrared image can be used to determine the fault and defect degree of the disconnector and provide maintenance. The model used in this study exhibits good accuracy and reliability.
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表 1 隔离开关温差故障判断标准
Table 1 Criteria for diagnosing temperature difference faults in isolation switches
Equipment status Temperature difference threshold Normal [0, 0.35] General defects [0.35, 0.80] Serious disadvantage [0.80, 0.95] Critical defect (0.95, 1] 
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表 2 检测窗口尺寸的影响
Table 2 Effects of window size on detection
Detecting window size Block size Number of gradient histograms RGB precision Lab
precision64×128 16×16 9 74.5% 76.3% 32×64 16×16 9 94.8% 97.5% 16×32 16×16 9 73.2% 75.4%
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表 3 块尺寸的影响
Table 3 Effects of block sizes
Detecting window size Block size Number of gradient histograms RGB precision Lab precision 32×64 16×16 9 94.8% 97.5% 32×64 8×16 9 74.5% 77.2% 32×64 8×8 9 70.9% 71.4%
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表 4 梯度直方图数量的影响
Table 4 The Influence of the number of gradient histograms
Detecting window size Block size Number of gradient histograms RGB precision Lab precision 32×64 16×16 9 94.8% 97.5% 32×64 16×16 6 91.7% 93.2% 32×64 16×16 3 63.1% 63.2%
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表 5 不同型号隔离开关的识别准确率
Table 5 Identification accuracy of different types of isolation switches
Model State The algorithm in this paper Neural network algorithm GW4 Open the gate 97.4% 96.8% GW4 Close the switch 98.3% 97.2% GW6 Open the gate 97.2% 95.3% GW6 Close the switch 98.1% 97.9% GW22 Open the gate 99.5% 99.6% GW22 Close the switch 98.7% 98.5%
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表 6 隔离开关故障诊断
Table 6 Fault diagnosis of isolation switch
Serial Number Highest temperature/℃ Relative temperature difference Fault diagnosis results 1 46.2 52.1% General defects 2 68.4 83.2% Serious malfunction 3 38.5 42.2% General defects 4 82.3 96.7% Critical defect 5 46.8 53.4% General defects
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